Cancer represents the phenotypic end-point of multiple genetic lesions that endow cells with a full range of biological properties required for malignancy and tumorigenesis. Indeed, a hallmark genomic feature of many cancers is the presence of complex chromosome structural aberrations, including non-reciprocal translocations, amplifications and deletions.
Karyotype analyses (Johansson, B., et al. (1992) Cancer 69, 1674-81; Bardi, G., et al. (1993) Br J Cancer 67, 1106-12; Griffin, C. A., et al. (1994) Genes Chromosomes Cancer 9, 93-100; Griffin, C. A., et al. (1995) Cancer Res 55, 2394-9; Gorunova, L., et al. (1995) Genes Chromosomes Cancer 14, 259-66; Gorunova, L., et al. (1998) Genes Chromosomes Cancer 23, 81-99), chromosomal CGH and array CGH (Wolf M et al. (2004) Neoplasia 6(3)240; Kimura Y, et al. (2004) Mod. Pathol. 21 May (epub); Pinkel, et al. (1998) Nature Genetics 20:211; Solinas-Toldo, S., et al. (1996) Cancer Res 56, 3803-7; Mahlamaki, E. H., et al. (1997) Genes Chromosomes Cancer 20, 383-91; Mahlamaki, E. H., et al. (2002) Genes Chromosomes Cancer 35, 353-8; Fukushige, S., et al. (1997) Genes Chromosomes Cancer 19:161-9; Curtis, L. J., et al. (1998) Genomics 53, 42-55; Ghadimi, B. M., et al. (1999) Am J Pathol 154, 525-36; Armengol, G., et al. (2000) Cancer Genet Cytogenet 116, 133-41), fluorescence in situ hybridization (FISH) analysis (Nilsson M et al. (2004) Int J Cancer 109(3):363-9; Kawasaki K et al. (2003) Int J Mol Med. 12(5):727-31) and loss of heterozygosity (LOH) mapping (Wang Z C et al. (2004) Cancer Res 64(1):64-71; Seymour, A. B., et al. (1994) Cancer Res 54, 2761-4; Hahn, S. A., et al. (1995) Cancer Res 55, 4670-5; Kimura, M., et al. (1996) Genes Chromosomes Cancer 17, 88-93) have identified recurrent regions of copy number change or allelic loss in various cancers.
To date, however, such techniques have been applied with relatively low resolution and without concordant assessments of transcript abundance and copy number. Thus, the precise boundaries of copy number alterations (CNAs) and the identification of the underlying genes responsible for malignant transformation remained undefined, particularly among cancers (e.g., hematological cancers) known to have complex patterns of genetic instability. For example, diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma in adults and is characterized as both a clinically and genetically heterogenous disorder. With current immunochemotherapy, over 60% of patients with DLBCL can be cured. However, the remaining patients succumb to their disease (Friedberg, J. W. (2008) Hematol Oncol Clin North Am 22:941-949).
Given the numbers and types of genetic alterations in DLBCL, investigators have sought additional comprehensive classification systems to identify groups of tumors with similar molecular traits. Transcriptional profiling has been used to define DLBCL subsets that share certain features with normal B-cell subtypes (“cell-of-origin” classification, COO) (Lenz et al. (2010) New Engl J Med 362:1417-1429). COO-defined DLBCLs include “germinal center B-cell” (GCB) and “activated B-cell” (ABC) types and an additional group of unclassified tumors. The COO-defined tumor groups are characterized by certain biological features, most notably increased NFκB activity and less favorable outcome in ABC-type DLBCLs (Compagno et al. (2009) Nature 459:717-722 and Lenz et al. (2010) New Engl J Med 362:1417-1429). However, the outcome differences in GCB and ABC type DLBCLs may be less striking in patients treated with current Rituxan® (rituximab)-containing combination chemotherapy regimens (Fu et al. (2008) J Clin Oncol 26:4587-4594 and Lenz et al. (2008) New Engl J Med 359:2313-2323). An alternative transcriptional profiling classification, termed comprehensive consensus clustering (CCC), identifies DLBCL subtypes solely on the basis of distinctions within primary tumors and includes the 3 groups: “B-cell receptor” (BCR); “Oxidative Phosphorylation” (OxP); and “Host-response” (HR) (Chen et al. (2008) Blood 111:2230-2237 and Monti et al. (2005) Blood 105:1851-1861).
Despite such recent advances in the molecular understanding of DLBCL pathogenesis, however, clinical risk factor models are still used to identify patients who are unlikely to be cured with current therapy. The most widely used model is the International Prognostic Index (IPI), which is an outcome predictor based on easily measurable clinical parameters including age, performance status, serum LDH, Ann Arbor stage and numbers of extranodal disease sites (Shipp et al. (1993) N Engl J Med 329:987-994). Although the IPI is generally robust and reproducible, the link between the included clinical parameters and underlying biology remains to be defined and improved upon. In addition, the clinical model does not provide insights regarding alternative treatment approaches for high-risk patients.
In view of the above, it is clear that there remains a need in the art for methods and compositions to identify, assess, prevent, and treat cancers (e.g., hematological cancers, including DLBCL).